Content-aware image retargeting has attracted substantial research interests in the related research community. However, so far there is still no method can preserve important image contents and structure well without...
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ISBN:
(纸本)9781479999897
Content-aware image retargeting has attracted substantial research interests in the related research community. However, so far there is still no method can preserve important image contents and structure well without introducing deformation. To address this problem, we propose a Saliency & Structure Preserving Multi-operator (SSPM) method. SSPM classifies images into three categories utilizing SIFT density to improve performance of saliency preservation, helping to mitigate negative influence from center-bias property of most existing saliency detection models. SSPM also employs different principles to improve structure preservation performance, including Earth Mover's Distance (EMD) and Gray-Level Cooccurrence Matrix (GLCM) to get optimal operator sequences for smart content-aware image retargeting. SSPM method not only can well preserve salient contents and structure, but also can greatly improve deformation resilience. Experimental results demonstrated that our method outperforms state-of-art image retargeting methods.
This paper addresses the issue on how to more effectively coordinate the depth with RGB aiming at boosting the performance of RGB-D object detection. Particularly, we investigate two primary ideas under the CNN model:...
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This paper addresses the issue on how to more effectively coordinate the depth with RGB aiming at boosting the performance of RGB-D object detection. Particularly, we investigate two primary ideas under the CNN model: property derivation and property fusion. Firstly, we propose that the depth can be utilized not only as a type of extra information besides RGB but also to derive more visual properties for comprehensively describing the objects of interest. So a two-stage learning framework consisting of property derivation and fusion is constructed. Here the properties can be derived either from the provided color/depth or their pairs (e.g. the geometry contour adopted in this paper). Secondly, we explore the fusion method of different properties in feature learning, which is boiled down to, under the CNN model, from which layer the properties should be fused together. The analysis shows that different semantic properties should be learned separately and combined before passing into the final classifier. Actually, such a detection way is in accordance with the mechanism of the primary neural cortex (V1) in brain. We experimentally evaluate the proposed method on the challenging dataset, and have achieved state-of-the-art performance.
Content-aware image retargeting has attracted substantial research interests in the related research community. However, so far there is still no method can adequately preserve important image contents and structure w...
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ISBN:
(纸本)9781509053179
Content-aware image retargeting has attracted substantial research interests in the related research community. However, so far there is still no method can adequately preserve important image contents and structure well without introducing conspicuous visible deformation in a relatively short period of time. To address this problem, we propose a Fast Genetic Multi-operator (FGM) method which integrates multiple retargeting operators. To improve the efficiency, FGM method utilizes Genetic Algorithms (GAs) to reach the optimal operator ratio, which adopts saliency and Gray-Level Co-occurrence Matrix (GLCM) as its energy function. FGM method not only can well preserve salient contents and structure, but also can greatly reduce the computational complexity. Experimental results demonstrated that our method outperforms state-of-art image retargeting methods.
Visual Quality Assessment of 3D/stereoscopic video (3D VQA) is significant for both quality monitoring and optimization of the existing 3D video services. In this paper, we build a 3D video database based on the lates...
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ISBN:
(纸本)9781509003556
Visual Quality Assessment of 3D/stereoscopic video (3D VQA) is significant for both quality monitoring and optimization of the existing 3D video services. In this paper, we build a 3D video database based on the latest 3D-HEVC video coding standard, to investigate the relationship among video quality, depth quality, and overall quality of experience (QoE) of 3D/stereoscopic video. We also analyze the pivotal factors to the video and depth qualities. Moreover, we develop a No-Reference 3D-HEVC bitstream-level objective video quality assessment model, which utilizes the key features extracted from the 3D video bitstreams to assess the perceived quality of the stereoscopic video. The model is verified to be effective on our database as compared with widely used 2D Full-Reference quality metrics as well as a state-of-the-art 3D FR pixel-level video quality metric.
The directional intra prediction (DIP) modes in HEVC are capable of predicting local continuous image features. Recently, intra block copy (IBC) is proposed for screen content coding, aiming at predicting non-local re...
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The directional intra prediction (DIP) modes in HEVC are capable of predicting local continuous image features. Recently, intra block copy (IBC) is proposed for screen content coding, aiming at predicting non-local recurrent image features. For natural video, we observe that recurrent features are often irregular and not aligned with blocks. Thus, we propose a combination of DIP and IBC with block partition for better intra prediction, where one block can be divided into several partitions, each of which may choose between DIP and IBC. We study an intra prediction scheme with the proposed combination, especially the rate-distortion optimization and entropy coding in the scheme. Preliminary experimental results show that the proposed combined intra prediction achieves as high as 5.8% bit-rate saving compared to HEVC anchor.
In all of the existing block-based image and video coding standards, blocks are processed in the fixed scan order. Then in HEVC intra coding, intra prediction is always based on the top and/or left neighboring reconst...
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ISBN:
(纸本)9781509053179
In all of the existing block-based image and video coding standards, blocks are processed in the fixed scan order. Then in HEVC intra coding, intra prediction is always based on the top and/or left neighboring reconstructed pixels, which incurs less accurate prediction for blocks where the spatial correlation is not along the topleft-to-bottomright direction. To obtain better intra prediction, we propose to flexibly determine the coding order of blocks in HEVC intra coding. Complying with the hierarchical quadtree structure in HEVC, our flexible block ordering (FBO) technique recursively decides the coding order of four sub-blocks when splitting one block. Moreover, we propose new methods to perform inter/extrapolation for intra prediction so as to fully utilize neighboring reconstructed pixels, not always being top/left. Experimental results show that our proposed FBO technique achieves on average 2.9% BD-rate reduction compared to HEVC baseline.
Discrete Cosine Transform (DCT) has been the commonly used transform for a few decades in image/video coding. However, DCT does not work well on the blocks having anisotropic correlations. In this paper, based on the ...
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ISBN:
(纸本)9781479953424
Discrete Cosine Transform (DCT) has been the commonly used transform for a few decades in image/video coding. However, DCT does not work well on the blocks having anisotropic correlations. In this paper, based on the adaptive dictionary, we propose a new online transform scheme using Orthogonal Matching Pursuit (OMP) for High Efficiency Video Coding (HEVC). For a coding block, we construct its dictionary by exploiting non-local correlations from the reconstructed regions. The OMP algorithm is implemented to obtain the sparse transform coefficients. Experimental results show that the BD-rate savings of the proposed scheme for the sequences with strong edges can be up to 19.9%.
We propose a novel superpixel algorithm based on Minimum Spanning Tree (MST), to generate superpixels efficiently while strictly adhere to object boundaries. The MST, which built by gradually removing strong edges of ...
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ISBN:
(纸本)9781467372596
We propose a novel superpixel algorithm based on Minimum Spanning Tree (MST), to generate superpixels efficiently while strictly adhere to object boundaries. The MST, which built by gradually removing strong edges of the image graph extracted from the image, is more sensitive to image local structures. Therefore, an efficient hierarchical clustering strategy is basically employed in our algorithm to segment the input image into superpixels based on the tree distance. To gradually merge the image pixels and remove texture noises, a multi-layer scheme with different resolutions of superpixels is proposed. In each layer, the graph is constructed from the lower layer and segmented into superpixels in a linear complexity with the node number in the graph. Because the node number in each layer is exponentially reduced, the computational time of our method mainly concentrates on the first few layers, which is linear with the number of image pixels. The experimental results conducted on the Berkeley Segmentation Dataset demonstrate that our method outperforms state-of-the-art methods both in terms of structure preservation and computational efficiency.
Two approximations, center-beam approximation and reference digital elevation model （DEM） approximation, are used in synthetic aperture radar （SAR） motion compensation procedures. They usually introduce residual m...
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Two approximations, center-beam approximation and reference digital elevation model （DEM） approximation, are used in synthetic aperture radar （SAR） motion compensation procedures. They usually introduce residual motion compensation errors for airborne single-antenna SAR imaging and SAR interferometry. In this paper, we investigate the effects of residual uncompensated motion errors, which are caused by the above two approximations, on the performance of airborne along-track interferometric SAR （ATI-SAR）. The residual uncompensated errors caused by center-beam approximation in the absence and in the presence of elevation errors are derived, respectively. Airborne simulation parameters are used to verify the correctness of the analysis and to show the impacts of residual uncompensated errors on the interferometric phase errors for ATI-SAR. It is shown that the interferometric phase errors caused by the center-beam approximation with an accurate DEM could be neglected, while the interferometric phase errors caused by the center-beam approximation with an inaccurate DEM cannot be neglected when the elevation errors exceed a threshold. This research provides theoretical bases for the error source analysis and signal processing of airborne ATI-SAR.
InSAR interferogram quality assessment is a key step for the using of interferogram map. Traditionally, the interferogram is qualitatively assessed visually and quantitatively assessed by the number of residues. Howev...
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InSAR interferogram quality assessment is a key step for the using of interferogram map. Traditionally, the interferogram is qualitatively assessed visually and quantitatively assessed by the number of residues. However, the important structure information is hardly quantifiable. This paper presents a novel index to evaluate the quality of InSAR interferogram based on connected area. After discomposing the fringes into independent connected areas, we analyze the statistical ratio of an area to its margin. Then we use the ratio as an index to quantitatively evaluate the interferogram. In the end, the presented index is used for the filtered interferogram of popular filters, and the results fit the visual judging.
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